Advances in deep learning-based Artificial Intelligence techniques in gastrointestinal stromal tumors.

Corresponding author: 孟, cenci@163.com

DOI: 10.12201/bmr.202411.00057

•  

  Abstract: 【】Gastrointestinal stromal tumors (GISTs) which originate from the Cajal mesenchyme are the most common and potentially malignant mesenchymal tumors of mesenchymal origin in the digestive tract, which can occur anywhere in the gastrointestinal and abdominal cavities.The most common of the GISTs is gastric stromal tumor (GST) (60% to 70% ), at present, CT, conventional gastroenteroscopy and endoscopic ultrasonography (EUS) are mostly used to diagnose GISTs, and their definitive diagnosis relies on post-tumor resection histopathology and immunohistochemistry markers CD117 (or C-KIT) and DOG1.With the in-depth research of AI technology on digestive system diseases, the research of AI technology assisting the diagnosis and treatment of gastrointestinal mesenchymal stromal tumor has also made great progress.The research of artificial intelligence technology on gastrointestinal mesenchymal tumors includes the following two aspects: on the one hand, the diagnosis of gastrointestinal mesenchymal tumors is assisted by artificial intelligence technology, which is mostly based on white light endoscopy, ultrasound endoscopy, tumor pathology and CT imaging; on the other hand, the prediction of gastrointestinal mesenchymal tumors malignant potential, recurrence, metastasis and prognosis is predicted by artificial intelligence technology.This article provides a review of the research progress on the application of deep learning-based artificial intelligence technology in the diagnosis and treatment of gastrointestinal mesenchymal stromal tumor disease.

  Key words: Deep Learning; Artificial Intelligence; Gastrointestinal Stromal Tumors; summarize

  Submit time: 24 November 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
• html

• 图表

• SiWei, XuTing, LinJiayue, Cao Wenting, ZhuAiyong. A review of artificial intelligence technology in the screening of cognitive impairment. 2024. doi: 10.12201/bmr.202407.00048

  yanwencheng, lijunzhen, chenmei, 卢艳红. Artificial Intelligence for Stomatology: Application in Dental Endodontics. 2021. doi: 10.12201/bmr.202102.00003

  liuqingjin, wangrui, miaoyuanqing. Intelligent Detection of Silent Myocardial Ischemia Dynamic Electrocardiogram Based on Deep Learning. 2021. doi: 10.12201/bmr.202111.00009

  zhaorui, shixiuyuan, zhongxueran, renping, tianxueqin, liuchunping, youmao. Construction of clinical application evaluation path of medical artificial intelligence technology based on health technology evaluationRui Zhao, Xiuyuan Shi, Xueran Zhong, Chunping Liu, Ping Ren, Xueqin Tian, Mao You. 2022. doi: 10.12201/bmr.202203.00005

  GE Xiaoling. Application of Artificial Intelligence Large Models in Healthcare:a Survey. 2024. doi: 10.12201/bmr.202408.00039

  Dong Kun, Yang Fen, Yang Yang. Application and Thinking of Artificial Intelligence in General Practitioner Training. 2023. doi: 10.12201/bmr.202305.00011

  Gu Yao-wen, Li Jiao. Progress of Mining Electronic Health Records based on Unsupervised Deep Learning Methods. 2021. doi: 10.12201/bmr.202104.00013

  Han Tongyan, zhangjuan. DOI:Study on the combination of artificial intelligence and mind mapping in cultivating clinical thinking among interns. 2024. doi: 10.12201/bmr.202412.00023

  ZhengYanli, Han Fuhai, LI Shuyu, SU Wenxing. Application Status and Prospect of Artificial Intelligence Large Models in Medicine. 2023. doi: 10.12201/bmr.202312.00027

  cuitao, KOU De-shuang. The Research of Tongue Features Base on Deep Learning. 2024. doi: 10.12201/bmr.202404.00020

• ID	Submit time	Number	Download
  1	2024-09-27	bmr.202411.00057V1	Download

• Public  Anonymous  To author only Submit Comments